doc updates

examples/python-recipes/openai-sample.py

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+# pip install openai sqlite-vec
+
+from openai import OpenAI
+import sqlite3
+import sqlite_vec
+import struct
+from typing import List
+
+
+def serialize(vector: List[float]) -> bytes:
+    """serializes a list of floats into a compact "raw bytes" format"""
+    return struct.pack("%sf" % len(vector), *vector)
+
+
+sentences = [
+    "Capri-Sun is a brand of juice concentrate–based drinks manufactured by the German company Wild and regional licensees.",
+    "George V was King of the United Kingdom and the British Dominions, and Emperor of India, from 6 May 1910 until his death in 1936.",
+    "Alaqua Cox is a Native American (Menominee) actress.",
+    "Shohei Ohtani is a Japanese professional baseball pitcher and designated hitter for the Los Angeles Dodgers of Major League Baseball.",
+    "Tamarindo, also commonly known as agua de tamarindo, is a non-alcoholic beverage made of tamarind, sugar, and water.",
+]
+
+
+client = OpenAI()
+
+# change ':memory:' to a filepath to persist data
+db = sqlite3.connect(":memory:")
+db.enable_load_extension(True)
+sqlite_vec.load(db)
+db.enable_load_extension(False)
+
+db.execute(
+    """
+        CREATE TABLE sentences(
+          id INTEGER PRIMARY KEY,
+          sentence TEXT
+        );
+    """
+)
+
+with db:
+    for i, sentence in enumerate(sentences):
+        db.execute("INSERT INTO sentences(id, sentence) VALUES(?, ?)", [i, sentence])
+
+db.execute(
+    """
+        CREATE VIRTUAL TABLE vec_sentences USING vec0(
+          id INTEGER PRIMARY KEY,
+          sentence_embedding FLOAT[1536]
+        );
+    """
+)
+
+
+with db:
+    sentence_rows = db.execute("SELECT id, sentence FROM sentences").fetchall()
+    response = client.embeddings.create(
+        input=[row[1] for row in sentence_rows], model="text-embedding-3-small"
+    )
+    for (id, _), embedding in zip(sentence_rows, response.data):
+        db.execute(
+            "INSERT INTO vec_sentences(id, sentence_embedding) VALUES(?, ?)",
+            [id, serialize(embedding.embedding)],
+        )
+
+
+query = "fruity liquids"
+query_embedding = (
+    client.embeddings.create(input=query, model="text-embedding-3-small")
+    .data[0]
+    .embedding
+)
+
+results = db.execute(
+    """
+      SELECT
+        vec_sentences.id,
+        distance,
+        sentence
+      FROM vec_sentences
+      LEFT JOIN sentences ON sentences.id = vec_sentences.id
+      WHERE sentence_embedding MATCH ?
+        AND k = 3
+      ORDER BY distance
+    """,
+    [serialize(query_embedding)],
+).fetchall()
+
+for row in results:
+    print(row)